Nature-inspired computation and swarm intelligence algorithms, theory and applications / edited by Xin-She Yang.

Format:

Book

Contributor:

Yang, Xin-She.

ScienceDirect (Online service)

Language:

English

Subjects (All):

Natural computation.

Swarm intelligence.

Physical Description:

1 online resource

Place of Publication:

London : Academic Press, 2020.

System Details:

text file

Contents:

Front Cover

Nature-Inspired Computation and Swarm Intelligence

Copyright

Contents

List of contributors

About the editor

Preface

Acknowledgments

Part 1 Algorithms

1 Nature-inspired computation and swarm intelligence: a state-of-the-art overview

1.1 Introduction

1.2 Optimization and optimization algorithms

1.2.1 Mathematical formulations

1.2.2 Gradient-based algorithms

1.2.3 Gradient-free algorithms

1.3 Nature-inspired algorithms for optimization

1.3.1 Genetic algorithms

1.3.2 Ant colony optimization

1.3.3 Differential evolution

1.3.4 Particle swarm optimization

1.3.5 Fire y algorithm

1.3.6 Cuckoo search

1.3.7 Bat algorithm

1.3.8 Flower pollination algorithm

1.3.9 Other algorithms

1.4 Algorithms and self-organization

1.4.1 Algorithmic characteristics

1.4.2 Comparison with traditional algorithms

1.4.3 Self-organized systems

1.5 Open problems for future research

References

2 Bat algorithm and cuckoo search algorithm

2.1 Introduction

2.2 Bat algorithm

2.2.1 Algorithmic equations of BA

2.2.2 Pulse emission and loudness

2.2.3 Pseudocode and parameters

2.2.4 Demo implementation

2.3 Cuckoo search algorithm

2.3.1 Cuckoo search

2.3.2 Pseudocode and parameters

2.3.3 Demo implementation

2.4 Discretization and solution representations

3 Fire y algorithm and ower pollination algorithm

3.1 Introduction

3.2 The re y algorithm

3.2.1 Algorithmic equations in FA

3.2.2 FA pseudocode

3.2.3 Scalings and parameters

3.2.4 Demo implementation

3.2.5 Multiobjective FA

3.3 Flower pollination algorithm

3.3.1 FPA pseudocode and parameters

3.3.2 Demo implementation

3.4 Constraint handling

3.5 Applications

4 Bio-inspired algorithms: principles, implementation, and applications to wireless communication

4.1 Introduction

4.2 Selected bio-inspired techniques: principles and implementation

4.2.1 Genetic algorithm

4.2.2 Differential evolution

4.2.3 Particle swarm optimization

4.2.4 Bacterial foraging optimization

4.3 Application of bio-inspired optimization techniques in wireless communication

4.3.1 Bio-inspired techniques for direct modeling application

4.3.2 Bio-inspired techniques for inverse modeling application

4.3.3 Bio-inspired techniques for mobility management in cellular networks

4.3.4 Bio-inspired techniques for cognitive radio-based Internet of Things

4.4 Conclusion

Part 2 Theory

5 Mathematical foundations for algorithm analysis

5.1 Introduction

5.2 Optimization and optimality

5.3 Norms

5.4 Eigenvalues and eigenvectors

5.5 Convergence sequences

5.6 Series

5.7 Computational complexity

5.8 Convexity

6 Probability theory for analyzing nature-inspired algorithms

6.1 Introduction

6.2 Random variables and probability

Notes:

Includes index.

Electronic reproduction. Amsterdam Available via World Wide Web.

Other Format:

Print version:

ISBN:

9780128226094

0128226099

Publisher Number:

99987371890

Access Restriction:

Restricted for use by site license.